SAS Visual Forecasting

Titre Niveau Types de formation
FDP-Shaping a Data Science Curriculum
This FDP supports developing a data science program that covers a variety of topics and enables students to acquire the skills that industry is looking for their employees to have. The FDP helps universities develop a pool of talent with the range of analytical and technology skills to work in a data-rich business environment.

1 Débutant Live Web Classroom
La prévision en utilisant Model Studio de SAS Viya
Cette formation vous propose un tour d’horizon des fonctionnalités de prévision du composant Model Studio de SAS Viya.Vous verrez comment charger en mémoire et comment visualiser selon leurs caractéristiques des données temporelles avant de les modéliser. Vous découvrirez la notion de chemin pour générer des prévisions et sélectionner après comparaison le modèle champion ainsi que la notion de prévisions à grande-échelle.

2 Fondamentaux Classroom Live Web Classroom e-Learning
Large-Scale Forecasting Using SAS Viya: A Programming Approach
This course teaches students how to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. For the course project, students build and then refine a large-scale forecasting system. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Students are then asked to improve overall baseline forecasting performance by modifying default processes in the system.

3 Intermédaire Classroom Live Web Classroom e-Learning
Time Series Feature Mining and Creation
In this course, you learn about data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.

3 Intermédaire Classroom Live Web Classroom e-Learning
Models for Time Series and Sequential Data
This course teaches students to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data (or ARMA) to models for trend and seasonality (ARIMA) and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms. The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations of creating combined (or ensemble) and hybrid model forecasts.

3 Intermédaire Classroom Live Web Classroom e-Learning